22 skills found
Aryia-Behroziuan / NeuronsAn ANN is a model based on a collection of connected units or nodes called "artificial neurons", which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit information, a "signal", from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called "edges". Artificial neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer (the input layer) to the last layer (the output layer), possibly after traversing the layers multiple times. The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis. Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition.[68] Decision trees Main article: Decision tree learning Decision tree learning uses a decision tree as a predictive model to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). It is one of the predictive modeling approaches used in statistics, data mining, and machine learning. Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. In data mining, a decision tree describes data, but the resulting classification tree can be an input for decision making. Support vector machines Main article: Support vector machines Support vector machines (SVMs), also known as support vector networks, are a set of related supervised learning methods used for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other.[69] An SVM training algorithm is a non-probabilistic, binary, linear classifier, although methods such as Platt scaling exist to use SVM in a probabilistic classification setting. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces. Illustration of linear regression on a data set. Regression analysis Main article: Regression analysis Regression analysis encompasses a large variety of statistical methods to estimate the relationship between input variables and their associated features. Its most common form is linear regression, where a single line is drawn to best fit the given data according to a mathematical criterion such as ordinary least squares. The latter is often extended by regularization (mathematics) methods to mitigate overfitting and bias, as in ridge regression. When dealing with non-linear problems, go-to models include polynomial regression (for example, used for trendline fitting in Microsoft Excel[70]), logistic regression (often used in statistical classification) or even kernel regression, which introduces non-linearity by taking advantage of the kernel trick to implicitly map input variables to higher-dimensional space. Bayesian networks Main article: Bayesian network A simple Bayesian network. Rain influences whether the sprinkler is activated, and both rain and the sprinkler influence whether the grass is wet. A Bayesian network, belief network, or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independence with a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms exist that perform inference and learning. Bayesian networks that model sequences of variables, like speech signals or protein sequences, are called dynamic Bayesian networks. Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty are called influence diagrams. Genetic algorithms Main article: Genetic algorithm A genetic algorithm (GA) is a search algorithm and heuristic technique that mimics the process of natural selection, using methods such as mutation and crossover to generate new genotypes in the hope of finding good solutions to a given problem. In machine learning, genetic algorithms were used in the 1980s and 1990s.[71][72] Conversely, machine learning techniques have been used to improve the performance of genetic and evolutionary algorithms.[73] Training models Usually, machine learning models require a lot of data in order for them to perform well. Usually, when training a machine learning model, one needs to collect a large, representative sample of data from a training set. Data from the training set can be as varied as a corpus of text, a collection of images, and data collected from individual users of a service. Overfitting is something to watch out for when training a machine learning model. Federated learning Main article: Federated learning Federated learning is an adapted form of distributed artificial intelligence to training machine learning models that decentralizes the training process, allowing for users' privacy to be maintained by not needing to send their data to a centralized server. This also increases efficiency by decentralizing the training process to many devices. For example, Gboard uses federated machine learning to train search query prediction models on users' mobile phones without having to send individual searches back to Google.[74] Applications There are many applications for machine learning, including: Agriculture Anatomy Adaptive websites Affective computing Banking Bioinformatics Brain–machine interfaces Cheminformatics Citizen science Computer networks Computer vision Credit-card fraud detection Data quality DNA sequence classification Economics Financial market analysis[75] General game playing Handwriting recognition Information retrieval Insurance Internet fraud detection Linguistics Machine learning control Machine perception Machine translation Marketing Medical diagnosis Natural language processing Natural language understanding Online advertising Optimization Recommender systems Robot locomotion Search engines Sentiment analysis Sequence mining Software engineering Speech recognition Structural health monitoring Syntactic pattern recognition Telecommunication Theorem proving Time series forecasting User behavior analytics In 2006, the media-services provider Netflix held the first "Netflix Prize" competition to find a program to better predict user preferences and improve the accuracy of its existing Cinematch movie recommendation algorithm by at least 10%. A joint team made up of researchers from AT&T Labs-Research in collaboration with the teams Big Chaos and Pragmatic Theory built an ensemble model to win the Grand Prize in 2009 for $1 million.[76] Shortly after the prize was awarded, Netflix realized that viewers' ratings were not the best indicators of their viewing patterns ("everything is a recommendation") and they changed their recommendation engine accordingly.[77] In 2010 The Wall Street Journal wrote about the firm Rebellion Research and their use of machine learning to predict the financial crisis.[78] In 2012, co-founder of Sun Microsystems, Vinod Khosla, predicted that 80% of medical doctors' jobs would be lost in the next two decades to automated machine learning medical diagnostic software.[79] In 2014, it was reported that a machine learning algorithm had been applied in the field of art history to study fine art paintings and that it may have revealed previously unrecognized influences among artists.[80] In 2019 Springer Nature published the first research book created using machine learning.[81] Limitations Although machine learning has been transformative in some fields, machine-learning programs often fail to deliver expected results.[82][83][84] Reasons for this are numerous: lack of (suitable) data, lack of access to the data, data bias, privacy problems, badly chosen tasks and algorithms, wrong tools and people, lack of resources, and evaluation problems.[85] In 2018, a self-driving car from Uber failed to detect a pedestrian, who was killed after a collision.[86] Attempts to use machine learning in healthcare with the IBM Watson system failed to deliver even after years of time and billions of dollars invested.[87][88] Bias Main article: Algorithmic bias Machine learning approaches in particular can suffer from different data biases. A machine learning system trained on current customers only may not be able to predict the needs of new customer groups that are not represented in the training data. When trained on man-made data, machine learning is likely to pick up the same constitutional and unconscious biases already present in society.[89] Language models learned from data have been shown to contain human-like biases.[90][91] Machine learning systems used for criminal risk assessment have been found to be biased against black people.[92][93] In 2015, Google photos would often tag black people as gorillas,[94] and in 2018 this still was not well resolved, but Google reportedly was still using the workaround to remove all gorillas from the training data, and thus was not able to recognize real gorillas at all.[95] Similar issues with recognizing non-white people have been found in many other systems.[96] In 2016, Microsoft tested a chatbot that learned from Twitter, and it quickly picked up racist and sexist language.[97] Because of such challenges, the effective use of machine learning may take longer to be adopted in other domains.[98] Concern for fairness in machine learning, that is, reducing bias in machine learning and propelling its use for human good is increasingly expressed by artificial intelligence scientists, including Fei-Fei Li, who reminds engineers that "There’s nothing artificial about AI...It’s inspired by people, it’s created by people, and—most importantly—it impacts people. It is a powerful tool we are only just beginning to understand, and that is a profound responsibility.”[99] Model assessments Classification of machine learning models can be validated by accuracy estimation techniques like the holdout method, which splits the data in a training and test set (conventionally 2/3 training set and 1/3 test set designation) and evaluates the performance of the training model on the test set. In comparison, the K-fold-cross-validation method randomly partitions the data into K subsets and then K experiments are performed each respectively considering 1 subset for evaluation and the remaining K-1 subsets for training the model. In addition to the holdout and cross-validation methods, bootstrap, which samples n instances with replacement from the dataset, can be used to assess model accuracy.[100] In addition to overall accuracy, investigators frequently report sensitivity and specificity meaning True Positive Rate (TPR) and True Negative Rate (TNR) respectively. Similarly, investigators sometimes report the false positive rate (FPR) as well as the false negative rate (FNR). However, these rates are ratios that fail to reveal their numerators and denominators. The total operating characteristic (TOC) is an effective method to express a model's diagnostic ability. TOC shows the numerators and denominators of the previously mentioned rates, thus TOC provides more information than the commonly used receiver operating characteristic (ROC) and ROC's associated area under the curve (AUC).[101] Ethics Machine learning poses a host of ethical questions. Systems which are trained on datasets collected with biases may exhibit these biases upon use (algorithmic bias), thus digitizing cultural prejudices.[102] For example, using job hiring data from a firm with racist hiring policies may lead to a machine learning system duplicating the bias by scoring job applicants against similarity to previous successful applicants.[103][104] Responsible collection of data and documentation of algorithmic rules used by a system thus is a critical part of machine learning. Because human languages contain biases, machines trained on language corpora will necessarily also learn these biases.[105][106] Other forms of ethical challenges, not related to personal biases, are more seen in health care. There are concerns among health care professionals that these systems might not be designed in the public's interest but as income-generating machines. This is especially true in the United States where there is a long-standing ethical dilemma of improving health care, but also increasing profits. For example, the algorithms could be designed to provide patients with unnecessary tests or medication in which the algorithm's proprietary owners hold stakes. There is huge potential for machine learning in health care to provide professionals a great tool to diagnose, medicate, and even plan recovery paths for patients, but this will not happen until the personal biases mentioned previously, and these "greed" biases are addressed.[107] Hardware Since the 2010s, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks (a particular narrow subdomain of machine learning) that contain many layers of non-linear hidden units.[108] By 2019, graphic processing units (GPUs), often with AI-specific enhancements, had displaced CPUs as the dominant method of training large-scale commercial cloud AI.[109] OpenAI estimated the hardware compute used in the largest deep learning projects from AlexNet (2012) to AlphaZero (2017), and found a 300,000-fold increase in the amount of compute required, with a doubling-time trendline of 3.4 months.[110][111] Software Software suites containing a variety of machine learning algorithms include the following: Free and open-source so
ginking / Archimedes 1Archimedes 1 is a bot based sentient based trader, heavily influenced on forked existing bots, with a few enhancements here or there, this was completed to understand how the bots worked to roll the forward in our own manner to our own complete ai based trading system (Archimedes 2:0) This bot watches [followed accounts] tweets and waits for them to mention any publicly traded companies. When they do, sentiment analysis is used determine whether the opinions are positive or negative toward those companies. The bot then automatically executes trades on the relevant stocks according to the expected market reaction. The code is written in Python and is meant to run on a Google Compute Engine instance. It uses the Twitter Streaming APIs (however new version) to get notified whenever tweets within remit are of interest. The entity detection and sentiment analysis is done using Google's Cloud Natural Language API and the Wikidata Query Service provides the company data. The TradeKing (ALLY) API does the stock trading (changed to ALLY). The main module defines a callback where incoming tweets are handled and starts streaming user's feed: def twitter_callback(tweet): companies = analysis.find_companies(tweet) if companies: trading.make_trades(companies) twitter.tweet(companies, tweet) if __name__ == "__main__": twitter.start_streaming(twitter_callback) The core algorithms are implemented in the analysis and trading modules. The former finds mentions of companies in the text of the tweet, figures out what their ticker symbol is, and assigns a sentiment score to them. The latter chooses a trading strategy, which is either buy now and sell at close or sell short now and buy to cover at close. The twitter module deals with streaming and tweeting out the summary. Follow these steps to run the code yourself: 1. Create VM instance Check out the quickstart to create a Cloud Platform project and a Linux VM instance with Compute Engine, then SSH into it for the steps below. The predefined machine type g1-small (1 vCPU, 1.7 GB memory) seems to work well. 2. Set up auth The authentication keys for the different APIs are read from shell environment variables. Each service has different steps to obtain them. Twitter Log in to your Twitter account and create a new application. Under the Keys and Access Tokens tab for your app you'll find the Consumer Key and Consumer Secret. Export both to environment variables: export TWITTER_CONSUMER_KEY="<YOUR_CONSUMER_KEY>" export TWITTER_CONSUMER_SECRET="<YOUR_CONSUMER_SECRET>" If you want the tweets to come from the same account that owns the application, simply use the Access Token and Access Token Secret on the same page. If you want to tweet from a different account, follow the steps to obtain an access token. Then export both to environment variables: export TWITTER_ACCESS_TOKEN="<YOUR_ACCESS_TOKEN>" export TWITTER_ACCESS_TOKEN_SECRET="<YOUR_ACCESS_TOKEN_SECRET>" Google Follow the Google Application Default Credentials instructions to create, download, and export a service account key. export GOOGLE_APPLICATION_CREDENTIALS="/path/to/credentials-file.json" You also need to enable the Cloud Natural Language API for your Google Cloud Platform project. TradeKing (ALLY) Log in to your TradeKing (ALLY account and create a new application. Behind the Details button for your application you'll find the Consumer Key, Consumer Secret, OAuth (Access) Token, and Oauth (Access) Token Secret. Export them all to environment variables: export TRADEKING_CONSUMER_KEY="<YOUR_CONSUMER_KEY>" export TRADEKING_CONSUMER_SECRET="<YOUR_CONSUMER_SECRET>" export TRADEKING_ACCESS_TOKEN="<YOUR_ACCESS_TOKEN>" export TRADEKING_ACCESS_TOKEN_SECRET="<YOUR_ACCESS_TOKEN_SECRET>" Also export your TradeKing (ALLY) account number, which you'll find under My Accounts: export TRADEKING_ACCOUNT_NUMBER="<YOUR_ACCOUNT_NUMBER>" 3. Install dependencies There are a few library dependencies, which you can install using pip: $ pip install -r requirements.txt 4. Run the tests Verify that everything is working as intended by running the tests with pytest using this command: $ export USE_REAL_MONEY=NO && pytest *.py --verbose 5. Run the benchmark The benchmark report shows how the current implementation of the analysis and trading algorithms would have performed against historical data. You can run it again to benchmark any changes you may have made: $ ./benchmark.py > benchmark.md 6. Start the bot Enable real orders that use your money: $ export USE_REAL_MONEY=YES Have the code start running in the background with this command: $ nohup ./main.py & License Archimedes (edits under Invacio) Max Braun Frame under Max Braun, licence under Apache V2 License. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
behavioral-ds / BirdSpotterBirdSpotter is a python package which provides an influence and bot detection toolkit for twitter.
jddeguia / Compare Forecast ModelsEnergy production of photovoltaic (PV) system is heavily influenced by solar irradiance. Accurate prediction of solar irradiance leads to optimal dispatching of available energy resources and anticipating end-user demand. However, it is difficult to do due to fluctuating nature of weather patterns. In the study, neural network models were defined to predict solar irradiance values based on weather patterns. Models included in the study are artificial neural network, convolutional neural network, bidirectional long-short term memory (LSTM) and stacked LSTM. Preprocessing methods such as data normalization and principal component analysis were applied before model training. Regression metrics such as mean squared error (MSE), maximum residual error (max error), mean absolute error (MAE), explained variance score (EVS), and regression score function (R2 score), were used to evaluate the performance of model prediction. Plots such as prediction curves, learning curves, and histogram of error distribution were also considered as well for further analysis of model performance. All models showed that it is capable of learning unforeseen values, however, stacked LSTM has the best results with the max error, R2, MAE, MSE, and EVS values of 651.536, 0.953, 41.738, 5124.686, and 0.946, respectively.
deadskull7 / Rossmann Store Sales PredictionsKaggle top performer(Grandmaster) had a score of 0.10021. I had a self validation score of 0.10874 and a public score of 0.12516. Rossmann operates over 3,000 drug stores in 7 European countries. Currently, Rossmann store managers are tasked with predicting their daily sales for up to six weeks in advance. Store sales are influenced by many factors, including promotions, competition, school and state holidays, seasonality, and locality. With thousands of individual managers predicting sales based on their unique circumstances, the accuracy of results can be quite varied. Prediction is of 6 weeks of daily sales for 1,115 stores located across Germany.
Jenny1228 / Analysis On Relation Between Real Time News Sentiment And Stock Market PortfolioIn this project, we use two sets of data to draw insights on how media sentiment can be an indicator for the financial sector. For the financial data, we plan to use daily return of the market index <font color='green'>(^GSPC)</font>, which is a good indicator for market fluctuation; for media sentiment, we use summarized information of news pieces from top 10 most popular press because of their stronger influence in shaping people's perception of events that are happening in the world.** **Both sets of data are real-time, which means the source files are of the moment and need to be loaded each time analysis is performed. The sentiment analysis library returns a <font color='green'>polarity</font> score (-1.0 to 1.0) and a <font color='green'>polarity</font> score (0.0 to 1.0) on the news stories. Using quantified sentiment analysis, we juxtapose the two time series of data and observe if they present any correlation and search for potential causality. For example, we may test the hypothesis that when polarity among the daily news posts is higher (a.k.a., positive), the financial market that same day is more likely to rise. The rest of the notebook is a step-by-step instruction.
lilygrace454 / Mcafee.com ActivateThe Best Antivirus and Total Protection for Mac! What's the Best Malware Protection? Malware, Spyware, and Adware Protection Antivirus programming is basic for each PC. Without it, you hazard losing your own data, your records, and even the money from your financial balance. We've tried in excess of 40 utilities to enable you to pick the best antivirus security for your PCs. Malware, Spyware, and Adware Protection Summer is practically here, and we're all anticipating a stunning get-away, be it at the shoreline, in the mountains, or even on a journey. Malware coders get-aways, as well. Theirs is an occupation, similar to some other, from numerous points of view. In any case, that doesn't mean you'll be sheltered from infections, ransomware, bots, and other malware this late spring. The malware office director timetables get-aways, much the same as any office supervisor, to ensure someone's at work, making new assaults on your gadgets and your information. Before you head out, check your antivirus membership to ensure it won't take its very own get-away soon. In case you're not ensured at this point, put introducing an antivirus on your agenda. We've tried and McAfee com activate antivirus instruments so you can pick one and unwind with no stresses. We call it antivirus, yet in truth it's far-fetched you'll get hit with a real PC infection. Malware nowadays is tied in with profiting, and there's no simple method to take advantage of spreading an infection. Ransomware and information taking Trojans are considerably more typical, as are bots that let the bot-herder lease your PC for loathsome purposes. Current antivirus utilities handle Trojans, rootkits, spyware, adware, ransomware, and that's only the tip of the iceberg. PCMag has explored in excess of 40 distinctive business antivirus utilities, and that is not notwithstanding checking the many free antivirus devices. Out of that broad field we've named fourEditors' Choice items. mcafee activate product key antivirus utilities demonstrated powerful enough to procure an astounding four-star rating nearby their progressively conventional partners. VoodooSoft VoodooShield puts together its security with respect to stifling every obscure program while the PC is in a powerless state, for example, when it's associated with the web, and furthermore acts to identify known malware. The Kure resets the PC to a known safe state on each reboot, consequently wiping out any malware. On the off chance that you have malware, one of the ten items in the graph above should deal with the issue. You may see that one item in the outline earned simply 3.5 stars. The diagram had space for one more, and of the seven 3.5-star items, the labs just focus on F-Secure and G Data. F-Secure has the additional fillip of costing the equivalent for three licenses as most items charge for only one, so it advanced into the diagram. The blurbs at the base of this article incorporate each business antivirus that earned 3.5 stars or better. www.mcafee.com/activate offer insurance past the antivirus incorporated with Windows 10; the best free antivirus utilities additionally offer more. In any case, Microsoft Windows Defender Security Center is looking somewhat better recently, with some awesome scores from free testing labs. In our grasp on tests, it demonstrated a checked improvement since our past audit, enough to at long last bring it up to three stars. Tune in to the Labs We take the outcomes announced by free antivirus testing labs in all respects truly. The basic reality that a specific merchant's item appears in the outcomes is a demonstration of positive support, of sorts. It implies the lab considered the item huge, and the merchant felt the expense of testing was advantageous. Obviously, getting great scores in the tests is additionally significant. We pursue four labs that normally discharge nitty gritty reports: SE Labs, AV-Test Institute, MRG-Effitas, and AV-Comparatives. We likewise note whether sellers have contracted with ICSA Labs and West Coast labs for affirmation. We Test Malware, Spyware, and Adware Defenses We additionally subject each item to our very own hands-on trial of malware assurance, to a limited extent to get an inclination for how the item functions. Contingent upon how altogether the item averts malware establishment, it can acquire up to 10 for malware assurance. Our malware assurance test fundamentally utilizes a similar arrangement of tests for quite a long time. To check an item's treatment of fresh out of the box new malware, we test every item utilizing 100 amazingly new malware-facilitating URLs provided by MRG-Effitas, taking note of what level of them it blocked. Items get equivalent kudos for anticipating all entrance to the vindictive URL and for clearing out the malware during download. A few items win completely outstanding evaluations from the autonomous labs, yet don't toll too in our grasp on tests. In such cases, we concede to the labs, as they carry altogether more prominent assets to their testing. Need to know more? You can dive in for a point by point portrayal of how we test security programming. Multilayered Malware Protection Antivirus items separate themselves by going past the fundamentals of on-request examining and ongoing malware insurance. Some rate URLs that you visit or that appear in indexed lists, utilizing a red-yellow-green shading coding framework. Some effectively square procedures on your framework from associating with known malware-facilitating URLs or with false (phishing) pages. Programming has imperfections, and here and there those blemishes influence your mcafee security. Judicious clients keep Windows and all projects fixed, fixing those imperfections at the earliest opportunity. The defenselessness output offered by some antivirus items can check that every vital patches are available, and even apply any that are absent. Spyware comes in numerous structures, from concealed projects that log your each keystroke to Trojans that take on the appearance of legitimate projects while mining your own information. Any antivirus should deal with spyware, alongside every other sort of malware, yet some incorporate specific segments gave to spyware insurance. You expect an antivirus to recognize and dispose of terrible projects, and to disregard great projects. Shouldn't something be said about questions, programs it can't distinguish as positive or negative? Conduct based discovery can, in principle, secure you against malware that is so new scientists have never experienced it. In any case, this isn't generally an unmixed gift. It's normal for social discovery frameworks to hail numerous harmless practices performed by genuine projects. Whitelisting is another way to deal with the issue of obscure projects. A whitelist-based security framework just permits realized great projects to run. Questions are prohibited. This mode sometimes falls short for all circumstances, however it very well may be helpful. Sandboxing gives obscure projects a chance to run, however it segregates them from full access to your framework, so they can't do lasting damage. These different added layers serve to upgrade your assurance against malware. What's the Best Malware Protection? mcafee.com/activate antivirus would it be advisable for you to pick? You have an abundance of choices. Kaspersky Anti-Virus and Bitdefender Antivirus Plus routinely take impeccable or close ideal scores from the autonomous antivirus testing labs. A solitary membership for AntiVirus Plus gives you a chance to introduce security on the majority of your Windows, Android, Mac OS, and iOS gadgets. What's more, its unordinary conduct based recognition innovation implies Webroot SecureAnywhere Antivirus is the most diminutive antivirus around. We've named these four Editors' Choice for business antivirus, however they're by all account not the only items worth thought. Peruse the surveys of our top of the line items, and afterward settle on your own choice. Note that we have assessed a lot more antivirus utilities than we could incorporate into the diagram of top items. On the off chance that your preferred programming isn't recorded there, odds are we reviewed it. The blurbs beneath incorporate each item that oversaw 3.5 stars or better. Every one of the utilities recorded in this component are Windows antivirus applications. In case you're a macOS client, don't lose hope, be that as it may; PCMag has a different gathering devoted exclusively to the best Mac antivirus programming.
phl43 / Twitter ScoreA script that compute a score that is a better measure of someone's influence on Twitter than his number of followers. See this thread on Twitter for more details on the motivations and the computation of the score: https://twitter.com/phl43/status/946864280900653056.
asaficontact / Project FloodlightCrisis incidents caused by rebel groups create a negative influence on the political and economic situation of a country. However, information about rebel group activities has always been limited. Sometimes these groups do not take responsibility for their actions, sometimes they falsely claim responsibility for other rebel group’s actions. This has made identifying the rebel group responsible for a crisis incident a significant challenge. Project Floodlight aims to utilize different machine learning techniques to understand and analyze activity patterns of 17 major rebel groups in Asia (including Taliban, Islamic State, and Al Qaeda). It uses classification algorithms such as Random Forest and XGBoost to predict the rebel group responsible for organizing a crisis event based on 14 different characteristics including number of fatalities, location, event type, and actor influenced. The dataset used comes from the Armed Conflict Location & Event Data Project (ACLED) which is a disaggregated data collection, analysis and crisis mapping project. The dataset contains information on more than 78000 incidents caused by rebel groups that took place in Asia from 2017 to 2019. Roughly 48000 of these observations were randomly selected and used to develop and train the model. The final model had an accuracy score of 84% and an F1 Score of 82% on testing dataset of about 30000 new observations that the algorithm had never seen. The project was programmed using Object Oriented Programming in Python in order to make it scalable. Project Floodlight can be further expended to understand other crisis events in Asia and Africa such as protests, riots, or violence against women.
amit21AIT / Artifitial Neural Network Churn ModelingBusiness Problem: Dataset of a bank with 10,000 customers measured lots of attributes of the customer and is seeing unusual churn rates at a high rate. Want to understand what the problem is, address the problem, and give them insights. 10,000 is a sample, millions of customer across Europe. Took a sample of 10,000 measured six months ago lots of factors (name, credit score, grography, age, tenure, balance, numOfProducts, credit card, active member, estimated salary, exited, etc.). For these 10,000 randomly selected customers and track which stayed or left. Goal: create a geographic segmentation model to tell which of the customers are at highest risk of leaving. Valuable to any customer-oriented organisations. Geographic Segmentation Modeling can be applied to millions of scenarios, very valuable. (doesn't have to be for banks, churn rate, etc.). Same scenario works for (e.g. should this person get a loan or not? Should this be approved for credit => binary outcome, model, more likely to be reliable). Fradulant transactions (which is more likely to be fradulant) Binary outcome with lots of independent variables you can build a proper robust model to tell you which factors influence the outcome. alt text Problem: Classification problem with lots of independent variables (credit score, balance, number of products) and based on these variables we're predicting which of these customers will leave the bank. Artificial Neural Networks can do a terrific job with Classification problems and making those kind of predictions. Libraries used: Theano numerical computation library, very efficient for fast numerical computations based on Numpy syntax GPU is much more powerful than CPU, as there are many more cores and run more floating points calculations per second GPU is much more specialized for highly intensive computing tasks and parallel computations, exactly for the case for neural networks When we're forward propogating the activations of the different neurons in the neural network thanks to the activation function well that involves parallel computations When errors are backpropagated to the neural networks that again involves parallel computation GPU is a much better choice for deep neural network than CPU - simple neural networks, CPU is sufficient Created by Machine Learning group at the Univeristy of Montreal Tensorflow Another numerical computation library that runs very fast computations that can run on your CPU or GPU Google Brain, Apache 2.0 license Theano & Tensorflow are used primarily for research and development in the deep learning field Deep Learning neural network from scratch, use the above Great for inventing new deep learning neural networks, deep learning models, lots of line of code Keras Wrapper for Theano + Tensorflow Amazing library to build deep neural networks in a few lines of code Very powerful deep neural networks in few lines of code based on Theano and Tensorflow Sci-kit Learn (Machine Learning models), Keras (Deep Learning models) Installing Theano, Tensorflow in three steps with Anaconda installed: $ pip install theano $ pip install tensorflow $ pip install keras $ conda update --all
BenMiller3 / Social Media ScraperScrapes Instagram, Facebook, and Twitter of a person's name, and computes their social influence score.
sflagg01 / Red Wine QualityThis notebook analyzes the factors influencing perceived wine quality using linear regression on Kaggle's Red Wine Quality dataset. The dataset contains wine characteristics and a quality score on a 0-10 point scale. The aim is to explore how different features of the wine affect its perceived quality.
aiok03 / Final MiningDescriptive statistics and Explanatory data analysis In order to have an idea of the received data, we look through our table transactions and train. The shape of the train is 6000 rows and 2 columns (client_id and target – gender). Also we considered the info of transactions and noticed that there are no empty values, all of them are equal to 130039. After that we merged two tables and called it as data. To display unique codes and types we used ‘unique’ function and noticed that unique codes 173 and unique types 61. Using ‘describe’ function we can see minimal code, type, sum and the same parameters but maximum. The first hypothesis was to find what gender makes lots of requests. For conveniency we used for loop to make values in percentile view. And according to the barplot the biggest number of processes are made by females. The second hypothesis was to find the code with the biggest sum. For that we grouped by code and counted the mean of all sums. This list we converted from series to frame for further working process. The problem was that the code interpreted the code as the index, that’s why we have to fix it with ‘reset_index’ function. After that we plotted the graph and noticed that the most high sum is with 4722 code and proved it with another code under the graph. The third hypothesis is to find the distribution of sums relatively to the gender. But the first graph didn’t replaced this information because the scatter of the data is too high. The sign is not normally distributed and it is not symmetrical. It is hard to asses, that’s why we grouped information by gender and counted mean of the sum. According to this information we noticed that males spend more money than women. The same process we made with median and got the same conclusion. And since the mean and median values are not equal, our assumption about unnormalized data was proved. The last hypothesis was to find number of clients for each type and code – to find the most popular request within clients. For that we applied ‘str’ to each parameter for correct visualization on the graph. Counted the number of each request for type and code and reflected it in the graphs. According to them the most popular is 1010 type and 6011 code. Lastly, for further working process we returned type and code to the int type. Feature engineering Client’s balance condition We took every sum from dataframe data, grouped for every client and found the sum for each of them. We calculated the income and expenses for each client. Some clients with minus value made more expenses, some of them not, that means that he got more income. In minus is 0, in plus is 1. RFM In RFM section we started from Recency. For each client we grouped the information about them and found the maximum date where the transaction was done. The datetime column consisted from two values – date and time, for further working process in future engineering section we divided them for different columns. The most recent day we equaled to 457 and according to this value started to count the recency of last transactions for each client by subtraction. The next step is Frequency. We used ‘group by’ function and counted appearance of each client in our database. The last step is Monetary (to count expenses). Using group by function and condition, where the sum is less than 0 (expenses are negative values), we counted the total expenses of each client and noticed one point. That some clients didn’t spend any money at all. Segmentation based on RFM We merged all the tables into one and made a rank according to the best values in each segment using percentage. Using the formula we divided clients by 5 score scale, by this database and elbow method, plotted the graph, where 3 clusters were optimal solution. With KMeans library we plotted the k-mean illustration of clients according to the distance from randomly chosen centroids, showed distribution of clients in clusters. After the work done we gathered basic table with clusters using prefixes to each of them. Clustering for codes Now we'll work with codes to create clustering codes, and we'll utilize TF IDF and k-means to do it. We will also employ limitization, tokenization, and stop word elimination. We import the pymorphy2 library for limiting, and limiting is when words take their original form. Tokenization by sentences is the process of dividing a written language into component sentences. We also need to delete stop words, a stop word is a commonly used word (such as “the”, “a”, “an”, “in”) that a search engine has been programmed to ignore, both when indexing entries for searching and when retrieving them as the result of a search query. We would not want these words to take up space in our database, or taking up valuable processing time. We also make use of the re – Regular expression operations library, which is a library for regular expression operations. In this section we also use MorphAnalyzer() - Morphological analysis is the identification of a word's features based on how it is spelt. Morphological analysis does not make use of information about nearby words. For morphological analysis of words, there is a MorphAnalyzer class in pymorphy2. If we apply directly the clustering on those matrix, we will have issues as our matrices are very sparse and the computation of distances will be a mess. What we can do, is to perform IS to reduce data to a dense matrix of dimension 156 by applying SVD. Singular Value Decomposition (SVD) is one of the widely used methods for dimensionality reduction. We defined that 156 is the right number in our case. We used the Silhouette score to evaluate the quality of clusters created using K-Means. By Silhouette score we chose number of clusters and performed k means clustering on our tf-idf matrix. Then we tried to do a visualization of our clusters and we applied t-sne . t-SNE is a tool to visualize high-dimensional data. And then we added clusters to data and df dataframe. Finally we created word cloud by our clusters Clustering for types Data cleaning for types Firstly, we noticed that there were 155 types. However in data, there are 61 types. When we merge the data and that types, the total number of types become 58. This means that 3 types have no any description and that’s why we replace them with the mode value. Also we found that some types have type description ‘н.д’ which means no data and their total number in data is 26. Also we noticed that type description repeats for several types and we dropped duplicates and replaced them with first accurancy type in data. Creating clusters for types We manually divided them into the 5 categories according to dome key words in description. And merged them with our dataframe. Then we noticed outliers in recency and frequency. We found 0.999 and 0.001 quantile, where the first one is considered as the high, and the second is the low boundary. Everything above 0,999 and below 0.001 is considered as an outlier. We removed them for both recency and frequency. After that we checked dataframe by describe and concluded that everything become normal. Supervised learning The time for prediction came. We divided our dataframe into train and test and used KNN, Decision Tree Classifier and Random Forest, Logistic Regression for further predictions. We decided to investigate the accuracy from 1 to 20 with step 2 for each neighbor in train and test. And built the plot. The best result is accuracy 58 for 19 neighbors. Decision Tree gave us 54 for test set and Random Forest’s accuracy was 64. We investigated feature importance for both of them and noticed that monetary had the most influence on predicting the data. For Grid Search we manually set the hyper parameters and for cross validation equals to four folds. Best estimater for random forest classifier for grid search was found. After that good estimaters were chosen for random forest, and the same accuracy occurred. Best accuracy for random forest with default hyper parameters. We built confusion matrix and calculated recall, precision and f-1 score. Also we decided to build lofistic regression but the accuracy was too small, that’s why we build roc-auc and precision-recall curve. Conclusion All the models showed that taken data was not enough and actually not the best for gender prediction. Actions for increase the accuracy were done, such as adding more features, removing outliers. According to this investigation the best choice was random forest.
DrugowitschLab / ConnectomeInfluenceCalculatorInfluence score calculation using a linear dynamical model for neural signal propagation across the connectome.
Buzzkiller7 / Site Element Influence On Assessment ScoresNo description available
gokborayilmaz / Brand Influencer Monitoring AgentThis AI Agent monitors brand presence and influencer activity by retrieving the latest news articles and top social media influencers discussing a given brand or person. Using the Serp API, it gathers structured data, including article titles, descriptions, URLs, influencer names, platforms, engagement scores, and profile links. 🚀
NasonovIvan / ETIA FrameworkFramework of the Emotion-Aware Textual Influence Activation Score (ETIAS) to improve emotional prompt selection for LLMs. ETIAS measures emotion-related token focus, analyzing emotional activations across model layers.
purvi131 / ImpactLens An AI-powered legislative accountability system that compares attendance records with actual contributions like debates, bills proposed, and policy influence to generate a true Legislative Effectiveness Score for elected representatives.
VazquezJocelyn / Cosmetic Price AnalysisBuilt upon an existing open-source project to analyze cosmetic product pricing by examining ingredients, brand influence, and customer ratings. Enhanced the project with ingredient scoring, brand analysis, and data visualizations using Python and Sephora data.
sneha-rangole / Flight Price Prediction Using Random ForestThe Flight Price Prediction project utilizes Random Forest Regression to forecast flight prices based on historical data, empowering consumers and businesses to make informed decisions. With an impressive R² score of 0.812, the model effectively captures the complex relationships influencing airfare pricing.
